Zobrazeno 1 - 10
of 757
pro vyhledávání: '"Kalia, Rajiv K"'
Autor:
Gandhi, Yash, Zheng, Kexin, Jha, Birendra, Nomura, Ken-ichi, Nakano, Aiichiro, Vashishta, Priya, Kalia, Rajiv K.
Forecasting oil production from oilfields with multiple wells is an important problem in petroleum and geothermal energy extraction, as well as energy storage technologies. The accuracy of oil forecasts is a critical determinant of economic projectio
Externí odkaz:
http://arxiv.org/abs/2409.16482
Autor:
Nomura, Ken-ichi, Mishra, Ankit, Sang, Tian, Kalia, Rajiv K., Nakano, Aiichiro, Vashishta, Priya
Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of similar structural motifs in amorphous or glassy materials poses
Externí odkaz:
http://arxiv.org/abs/2312.05445
Autor:
Ibayashi, Hikaru, Razakh, Taufeq Mohammed, Yang, Liqiu, Linker, Thomas, Olguin, Marco, Hattori, Shinnosuke, Luo, Ye, Kalia, Rajiv K., Nakano, Aiichiro, Nomura, Ken-ichi, Vashishta, Priya
Neural-network quantum molecular dynamics (NNQMD) simulations based on machine learning are revolutionizing atomistic simulations of materials by providing quantum-mechanical accuracy but orders-of-magnitude faster, illustrated by ACM Gordon Bell pri
Externí odkaz:
http://arxiv.org/abs/2303.08169
Understanding oxidation mechanisms of layered semiconducting transition-metal dichalcogenide (TMDC) is important not only for controlling native oxide formation but also for synthesis of oxide and oxysulfide products. Here, reactive molecular dynamic
Externí odkaz:
http://arxiv.org/abs/2303.03220
Autor:
Liu, Kuang, Kalia, Rajiv K., Liu, Xinlian, Nakano, Aiichiro, Nomura, Ken-ichi, Vashishta, Priya, Zamora-Resendizc, Rafael
Machine learning (ML) is revolutionizing protein structural analysis, including an important subproblem of predicting protein residue contact maps, i.e., which amino-acid residues are in close spatial proximity given the amino-acid sequence of a prot
Externí odkaz:
http://arxiv.org/abs/2212.02251
Autor:
Powers, Connor, Bassman, Lindsay, Linker, Thomas, Nomura, Ken-ichi, Gulania, Sahil, Kalia, Rajiv K., Nakano, Aiichiro, Vashishta, Priya
Publikováno v:
SoftwareX 14, 100696 (2021)
We present MISTIQS, a Multiplatform Software for Time-dependent Quantum Simulations. MISTIQS delivers end-to-end functionality for simulating the quantum many-body dynamics of systems governed by time-dependent Heisenberg Hamiltonians across multiple
Externí odkaz:
http://arxiv.org/abs/2101.01817
Autor:
Ma, Ruru, Baradwaj, Nitish, Nomura, Ken-ichi, Krishnamoorthy, Aravind, Kalia, Rajiv K., Nakano, Aiichiro, Vashishta, Priya
Publikováno v:
Journal of Chemical Physics; 4/7/2024, Vol. 160 Issue 13, p1-20, 20p
Autor:
Mishra, Ankit, Chen, Lihua, Li, ZongZe, Nomura, Ken-ichi, Krishnamoorthy, Aravind, Fukushima, Shogo, Tiwari, Subodh C., Kalia, Rajiv K., Nakano, Aiichiro, Ramprasad, Rampi, Sotzing, Greg, Cao, Yang, Shimojo, Fuyuki, Vashishta, Priya
The increased energy and power density required in modern electronics poses a challenge for designing new dielectric polymer materials with high energy density while maintaining low loss at high applied electric fields. Recently, an advanced computat
Externí odkaz:
http://arxiv.org/abs/2011.09571
Autor:
Razakh, Taufeq Mohammed, Wang, Beibei, Jackson, Shane, Kalia, Rajiv K., Nakano, Aiichiro, Nomura, Ken-ichi, Vashishta, Priya
We have developed a novel differential equation solver software called PND based on the physics-informed neural network for molecular dynamics simulators. Based on automatic differentiation technique provided by Pytorch, our software allows users to
Externí odkaz:
http://arxiv.org/abs/2011.03490
Autor:
Krishnamoorthy, Aravind, Baradwaj, Nitish, Nakano, Aiichiro, Kalia, Rajiv K., Vashishta, Priya
Publikováno v:
Scientific Reports 11, 1656 (2021)
Engineering thermal transport in two dimensional materials, alloys and heterostructures is critical for the design of next-generation flexible optoelectronic and energy harvesting devices. Direct experimental characterization of lattice thermal condu
Externí odkaz:
http://arxiv.org/abs/2009.14508